Regional Economic Growth and the Heterogeneity Effect of Rail Network Connectivity in West Java Province

This paper aims at determining the heterogeneity effect of rail network connectivity influence on regional economic growth in West Java Province employing a multiple linear regression analysis approach to panel data covering 27 districts/cities in 2010-2018. There are several variables that are used as input variables for analysis such as GRDP per capita (PDRBPKit) as the dependent variable, connectivity (Konektivitasit) as the independent variable, and economic variables (Public Government Investment per capita (invpit); Private Investment per capita (invsit); Human Capital (ipmit); Agricultural Contribution (agricultureit); Industry Contribution (industriit); Service Contribution (jasait); and Population (populationit) as control variables. Some control variables show significant results in the heterogeneity effect. The results obtained show that the interaction of rail network connectivity with government investment and the contribution of the service sector is negative, the contribution of the industrial sector and population is positive.


1.
Introduction There are lots of empirical evidences showing that the availability of infrastructure has an influence on regional economic growth [1,2,3,4,5,6,7].Infrastructure is important as a catalyst in regional economic growth.However, many researchers forget or ignore the heterogeneity effect that arises from the infrastructure provision influence on regional economic growth based on differences in economic conditions between regions such as stock of capital, human resource capital, and level of urbanization [3,8].
Connectivity and accessibility in an area will get better with the addition of various transportation infrastructures such as roads, toll roads, airports, ports, as well as the rail network [23].The high connectivity of the rail network will encourage greater regional economic growth [9].The higher the level of connectivity from the development of the rail network, the higher the regional economic growth.The results of the analysis of Jiao et al. [9] confirm that improvements in accessibility and connectivity mainly arise from the rail network and have a strong influence on economic growth in China.
Morten and Oliveira [10] show in their study that improving transportation infrastructure has the potential not only to remove barriers to migration, but can also increase connectivity between regions, 1318 (2024) 012001 IOP Publishing doi:10.1088/1755-1315/1318/1/012001 2 thus facilitating knowledge spillover between regions through increased passenger mobility [11,12].Chong et al. [3] also confirm that increased connectivity from the development of transportation infrastructures play important role in facilitating economic growth.Further, Chong et al. [3] also found a heterogeneous effect of the effect of connectivity on economic growth based on differences in regional economic characteristics as indicated in resource capacity in terms of capital stock, human capital, and level of urbanization.
The acceleration of the implementation of railway infrastructure and facilities development projects in Java has contributed to higher connectivity in West Java Province.Geographical proximity to DKI Jakarta Province as the center of Indonesia's economic growth and connecting other provinces in Java in the railway line across the Jabodebek area (refer to Jakarta-Bogor-Depok-Bekasi areas), double track across North Java and across South Java, crossing The Jakarta-Bandung High Speed Railway, and the Jakarta-Surabaya semi-fast rail line, provide geostrategic benefits for West Java Province in increasing connectivity between districts/cities in West Java.
To improve connectivity between regions, in addition to the existing intercity rail network, a number of old rail networks in West Java will also be reactivated, including: Cibatu-Garut-Cikajang (extension of the Bandung-Cibatu line), Rancaekek-Tanjungsari (extension of the Bandung-Rancaekek line), Banjar-Pangandaran-Cijulang (extension of the Bandung-Banjar line), Bandung-Ciwidey, Cianjur-Bandung, and Cirebon-Kadipaten.The reactivation of the rail network will be carried out in line with the construction of the Kertajati airport rail network (Bandung -Kertajati/Majalengka Airport).The new track to Kertajati airport is planned to be built on the new Tanjungsari-Kadipaten-Kertajati and Arjawinangun-Kertajati lines using a double-track to the Pantura-Arjawinangun.This reactivation is a step by the central government, West Java Provincial government, and district/city governments to accelerate the flow of goods and people between regions in West Java through the rail network.
This paper with a focus on studies in West Java Province, uses panel data from 27 districts/cities in 2010-2018 to gain an in-depth understanding of the heterogeneity effects arising from the growing level of connectivity that arises due to the railroad network on regional economic growth as assessed by revenue value regional gross domestic per capita (PDRB per Capita).Researchers try to answer the question what variables have significant effects on the appearance of the heterogeneity effect of the influence of rail network connectivity.

2.
Literature Review Chong et al. [3] has identified that heterogeneity in the effect of connectivity on regional economic growth based on differences in economic conditions between regions as a control variable.In econometrics, the presence/absence of a heterogeneity effect is verified by testing the significance of the effect of the interaction factor between the independent variable (connectivity) and the control variable (economic conditions) on the dependent variable (economic growth).When the interaction factor is significant, the influence effect of the independent variables on the dependent variable tends to be heterogeneous or varies according to the different conditions of the control variables [8].Meanwhile, if the interaction factors are not significant, that effect tends to be homogeneous or uniform and does not depend on differences in the conditions of the control variables.
There are some studies indicating a heterogeneous transportation infrastructures effect on regional economic growth as found by [3,7,13] and has also been shown by Donaldson and Hornbeck [14].In the economic sphere, Sadovnichii et al. [6] stated that the development of the railway networks in Siberia and the Far East had several effects, namely accelerating the growth rate of the Russian economy's GDP, restructuring the economic structure from the primary sector to the secondary and tertiary sectors, the development of high technology-based industries, and in reducing the uneven development between regions.
The influence of transportation infrastructure on the growth of regional economy may vary widely because of regional characteristics differences.Transport quality needs to be carefully measured to reflect the heterogeneity of the infrastructure system [15].The benefits of transport networks may quite unclear, as the increased connectivity can potentially reshape the distribution of economic activity spatially, therefore widen the gap between the core and periphery areas [16].According to Chong et al. [3], though benefits are expected occurred from a developed transport network, there are possibilities that some cities may not achieve the benefit from an improved transport network because of lacking the adequate conditions (e.g., capital and labour) for the growth.Other studies revealed that even within the same region the benefits from transport infrastructure may become the expense of other places as there are evidences of negative spillovers from production factors mobility [20,21].Better connectivity through transportation infrastructure improvement may also enable the exchange between production factors and resources, and thus blurring the boundaries among cities within megapolitan, as have been shown in the case of high-speed rail development [19,22].In addition, Rokicki and Stępniak [17] found that there is positive correlation between increased accessibility of transportation infrastructure with regional employment growth.Instead, their results cast doubt on the positive relationship of large-scale infrastructure investment to regional economic growth in the long term.However, accessibility has a broader economic impact.In particular, investment in transport infrastructure reduces trade costs thereby creating regional economic specialization and more optimal land use.
Although there have been many studies in the field of transportation-economics using various approaches to measure the impact of increased accessibility of infrastructure on regional economic growth, there are very few researchers who consider the heterogeneity of effects arising from differences in economic conditions in each study area.So far only Chong et al. [3] who provide evidence that the effect of heterogeneity manifested by various characteristics of a regional economy such as capital, human resources, and urbanization, have a strong influence on the impact of rail network connectivity on regional economic growth.

Methods and Data
The data analysis method employed in this study is a multiple linear regression analysis for the panel data model using SPSS software.The Pooled Model is a panel data model where the intercept (constant) in the model has a fixed value for all observations and time, which is equal to b0.With regression analysis, it enables to measure the level of relationship between two or more variables, and also indicates the relationship direction between causal variables and effect variables.In this study, the causative variables are endogenous variables from the previous year (endogenous lag), independent variables, control variables, interaction variables between the control variables and the independent variables; and the interaction variables between the closeness spatial weight variables and the endogenous lag variables, independent variables, and control variables; while the effect variable is the dependent variable.In the model it is assumed that the effect variable is random, meaning it has a probabilistic distribution.It is also assumed that the causative variable has a fixed value [18].
In the panel data model regression analysis (pooled model), before testing the hypothesis, the classical assumption test is performed first.The classical assumption test is used to ensure that the data used is normally distributed and that the model does not contain multicollinearity and heteroscedasticity.In the classic assumption test in this study, the autocorrelation test was not included because the data formation being analyzed was in the form of panel data and not in the form of time series data (time-series), as referred to by Ghozali [18].We employ relevant secondary data for the variables in the model.All data are published by West Java provincial as well as national Central Bureau of Statistic office between 2010-2018.

Results and Discussion
The model for the influence of the interaction of connectivity with regional economic conditions on regional economic growth in West Java is formulated as follows: Ln (PDRBPKit) = β0 + β1 ln(PDRBPKi,t-1) + β2 konektivitasit + β3 ln(zit)
The results of testing the effect of connectivity, along with endogenous lag, control variables, and connectivity interactions with control variables, which are illustrated by government investment, on regional economic growth can be seen in the following table 1.

Table 1. Connectivity Interaction Effect Test Results with Government Investment on Regional Economic Growth
The regression equation of the model of the effect of the interaction of connectivity with government investment on regional economic growth in West Java based on the results of the analysis is: Ln (PDRBPKit) = 0,1123 + 1,0176 ln(PDRBPKi,t-1) + 0,0118 konektivitasit + 0,0018 ln(invpit) + 0,0003 ln(invsit) -0,0737 ln(ipmit) + 0,0008 ln(pertanianit) + 0,0045 ln(industriit) + 0,0395 ln(jasait) + 0,0052 ln(populasiit) -0,0010 konektivitasit x ln(invpit) + 0,0003 konektivitasit x ln(invsit) + 0,0038 konektivitasit x ln(ipmit) -0,0002 konektivitasit x ln(pertanianit) + 0,0010 konektivitasit x ln(industriit) -0,0011 konektivitasit x ln(jasait) + 0,0009 konektivitasit x ln(populasiit) + εi + σt + ξit in which: connectivity interaction with government investment konektivitasit x ln invsit = connectivity interaction with private investment konektivitasit x ln ipmit = connectivity interaction with HDI konektivitasit x ln pertanianit = connectivity interaction with agriculture contribution konektivitasit x ln industriit = connectivity interaction with industry contribution konektivitasit x ln jasait = connectivity interaction with service contribution konektivitasit x ln populasiit = connectivity interaction with population εi + σt + ξit = individual fixed effect, individual time effect, error factor The significance of the effect of connectivity, along with the endogenous lag, control variables, and the interaction of connectivity with government investment, is simultaneously tested on per capita GRDP by the F test.From the analysis results, the adjusted R square value is 0.99972 which is statistically significant.These results indicate the influence of connectivity, along with endogenous lag, control variables, and the interaction of connectivity with government investment, simultaneously on per capita GRDP.The calculated F value obtained is 72,958.291with a statistical error probability value (p-value) = 0.000.It appears that the p-value < (α = 0.05) or F > (F table = 1.879).The F table value is obtained from the F distribution table at α = 0.05 with degrees of freedom db1 = k = 10 (k = number of exogenous variables) and db2 = n-k-1 = 207-10-1 = 196 (n = number of data).Adjusted R square, adjusted coefficient of multiple determination, measures the magnitude of the effect of connectivity, along with endogenous lag and control variables, and the interaction of connectivity with government investment, simultaneously on GRDP per capita, which is 99.972%.The magnitude of this effect indicates the large variation of per capita GRDP that can be explained by connectivity, along with endogenous lag and control variables, and the interaction of connectivity with government investment in the model.The remaining per capita GRDP variation that is not explained by the model, that is explained by other factors not examined, is 1 -adjusted R square, which is 0.028%.The significance of the effect of connectivity, along with the endogenous lag and control variables, and the interaction of connectivity with government investment, partially to GRDP per capita is tested by t test.From the results of the analysis, it is obtained that the interaction coefficient of connectivity with government investment is β10 = -0.0010which is statistically significant.These results indicate a negative effect of the interaction of connectivity with government investment on per capita GRDP.The existence of a negative effect from this interaction indicates a heterogeneous effect of government investment which reduces the effect of rail network connectivity on regional economic growth.The calculated t value obtained is -1.996 with a probability value of statistical error (p-value) = 0.047.It appears that the p-value < (α = 0.05) or t < (-t table = -1.972).The value of t table is obtained from the t-student distribution table at α = 0.05 with degrees of freedom db = n-k-1 = 207-10-1 = 196 (k = number of exogenous variables and n = amount of data).The interaction coefficient of connectivity with government investment, namely the regression coefficient of the interaction of connectivity variables with government investment, measures the magnitude of the response to changes in GRDP per capita due to changes in the value of the interaction of connectivity with government investment in natural logarithmic units.The existence of a negative and significant interaction factor from government investment shows that regions with large capital resource support from government investment tend to receive a greater reduction in regional economic growth from increased connectivity due to the operation of the rail network.
As in the general model, the results of the analysis consistently show a positive endogeneity effect from the previous year's GRDP per capita (lag GRDP per capita) on GRDP per capita indicating that regional economic growth in West Java was also influenced by previous economic growth.From the results of the analysis, the lag coefficient of GRDP per capita is β1 = 1.0176 which is statistically significant with a p-value of 0.000.The existence of this positive endogenous effect indicates that regional economic growth in West Java depends positively on the increase in economic growth in the previous period.As in the general model, the control variables whose coefficients are statistically significant are also consistent, namely human capital, industry contribution, services contribution, and population with a coefficient value of β5 = -0.0737(p-value = 0.000); β7 = 0.0045 (p-value = 0.004); β8 = 0.0395 (p-value = 0.000); and β9 = 0.0052 (p-value = 0.000).
In the interaction model of connectivity with government investment, the results of the analysis show that there is a positive influence of connectivity on GRDP per capita at a significance level of α = 0.10 with a coefficient of β2 = 0.0118 (p-value = 0.063).These results indicate that in the interaction model, increased inter-regional connectivity from the development of the rail network has a positive effect on regional economic growth in West Java.The effect of this connectivity and the effect of connectivity interaction factors with government investment is the overall effect of connectivity to the rail network as represented by the overall connectivity regression coefficient, namely β2 + β10 ln(invpit) = 0.0118 + (-0, 0010 ln(invpit)).Because the connectivity effect is positive and significant, while the interaction factor effect is also negative and significant, this means that only areas with the support of capital resources from the government (government investment) that are lower than the threshold value tends to have more economic benefits from increased connectivity because of the operation of the rail network.The disadvantages of the interaction of connectivity with government investment are offset by the greater benefits due to the impact of connectivity.Large government investments that do not support the expansion of employment opportunities in the regions will produce a siphon effect in which increased rail network connections actually make it easier for workers to flow out to areas with higher employment opportunities.

Conclusions and recommendations
There is a heterogeneous effect of the influence of rail network connectivity on regional economic growth in West Java for regions with different economic conditions.Heterogeneous effects of government investment and service contributions are negative or reduce the positive effect of rail IOP Publishing doi:10.1088/1755-1315/1318/1/0120018 network connectivity on regional economic growth.Regions with large government investment and service sector contributions tend to receive more reductions in regional economic growth from increased connectivity.Government investment and a large service sector contribution that does not support the expansion of employment opportunities in the regions will tend to produce a siphon effect.
The heterogeneity effect of the contribution of industry and population is positive or compensates for the negative effect of connectivity.Regions with a large industrial sector and population contribution tend to receive more additional regional economic growth as compensation from increased connectivity.The low contribution of the industrial sector and a small population can experience the negative impact of low economic growth due to the siphon effect.
The findings of this study support the study by Chong et al. [3] which shows that there is a heterogeneous effect of the effect of connectivity on economic growth between cities as a contribution from differences in capital stock, human capital, and the level of urbanization.From a policy point of view, the pull effect that is owned by the region with the readiness of economic factors plays an important role in increasing regional welfare which is supported by the existence of transportation infrastructure, especially in this study the rail network.Therefore, this study provides new facts for policy makers to carry out specific strategies in the development of rail infrastructure so that the initial goal is to have a significant positive impact from economic and social aspects that can be felt by all regions.